Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11785
Title: Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems
Authors: Cai, T.
Wang, Q.
Zhang, S.
Demir, O.T.
Cavdar, C.
Keywords: antenna switching
BS control for energy saving
massive MIMO
multi-agent reinforcement learning
Energy efficiency
Energy utilization
Reinforcement learning
Sleep research
Antenna switching
Base station control for energy saving
Energy savings
Energy-savings
Massive multiple-input multiple-output
Multi agent
Multi-agent reinforcement learning
Multiple inputs
Multiple outputs
Policy optimization
Markov processes
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively. © 2024 IEEE.
Description: 1st IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2024 -- 5 May 2024 through 8 May 2024 -- Stockholm -- 201880
URI: https://doi.org/10.1109/ICMLCN59089.2024.10624787
https://hdl.handle.net/20.500.11851/11785
ISBN: 979-835034319-9
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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